################################
#	John Henderson
#	Gerrymandering Incumbency 
#		(with Brian Hamel and Aaron Goldzimer)
#		April 9, 2017    
#
################################
# code edits that produced by Chen & Rodden (2013), to replicate their 2000 analysis 
#  for competitiveness outcomes                                                 
################################
# calls sims2000.R to load data for plotting

rm(list=ls())    

GetColorHexAndDecimal <- function(color)
{
  c <- col2rgb(color)
  sprintf("#%02X%02X%02X %3d %3d %3d", c[1],c[2],c[3], c[1], c[2], c[3])
}

library(stringr)  
library(foreign)

setwd("~/Dropbox/StateRedistricting/replication/short/")
source('sims2000.R')

#########################
# cd flip prob
#########################


{
pdf(width=5,file='cd_2000-flip.pdf')
plot(x=-1000, y=-1000, type='n', xlim=c(-.18, 1), ylim=c(.5,20), axes=F, ylab="", xlab="")#, main=paste("Margins of Victory in House Elections\n",state,sep=""))
axis(1,at=c(0,.25,.5,.75,1),labels=c(0,.25,.5,.75,1))                                  
#cnts=0
cnts=20       
 
pcc=17
#legend(cex=.7,y=cnts-16.018,x=.65,border=F,box.lty=1,legend=c('Enacted Plan','Simulated Plan'),	
# col=c('red',gray(.65)),lwd=c(1,1),pch=c(1,19),lty=c(NA,NA))

#Means and Medians
for (state in states_ix){
	cnts=cnts-1
	state.dir <- paste(charts.dir,state,sep="/") 
	temp.sims <- t(sims[sims$st==state,-c(1:3)])
	temp.00 <- elections00[elections00$st==state, -c(1:3)]
	temp.02 <- elections02[elections02$st==state, -c(1:3)]

	#order margins
	for(i in 1:nrow(temp.sims)){
		temp.sims[i,]=sort(temp.sims[i,])
	}
    temp.00=sort(temp.00) 	
    temp.02=sort(temp.02)

	means.sim=apply(temp.sims,1,flipPr,state,year_mins,year_maxs)     
	mean.02=flipPr(temp.02,state,year_mins,year_maxs)
	ss.dif=mean(mean.02-means.sim)+.001

	#hypothesis-test-like statistics
	#mu=mean(means.sim,na.rm=T)
	#sds=sd(means.sim,na.rm=T) 
	#s.means.sim=((means.sim-mu)/sds)
	#s.mean.02=((mean.02-mu)/sds)	
	#s.dif=mean(s.mean.02-s.means.sim)
	if(ss.dif>0){
		p.dif=sum(mean.02<means.sim)/length(means.sim)    
	} else{                                                     
		p.dif=sum(mean.02>means.sim)/length(means.sim)    
	} 
	     
	cols='red' 
	ltys=2
	if(p.dif<.05){
		cols='red' 
		ltys=1
	}
	points(x=means.sim, y=jitter(factor=.2,rep(cnts,length(means.sim))), col=gray(.65), lwd=1,pch=pcc,cex=.52)
#	points(y=cnts, x=mean.02, col='red', pch=1,cex=.85) 
	points(y=cnts, x=mean.02, col=cols, pch=1,cex=abs(ss.dif)*8+.58,lty=2) 	
	#text(x=-.0,y=cnts,labels=as.character(state),srt = 1, pos = 2,cex=.65)	
	#text(adj=c(0,NA),x=-.0,y=cnts,labels=paste(as.character(state),' (',str_sub(s.dif,1,str_locate(s.dif,pattern='[/.]')[1]+2),')',sep=''),srt = 1, pos = 2,cex=.5)		
	text(adj=c(0,NA),x=-.11,y=cnts,labels=as.character(state),srt = 1, pos = 2,cex=.65)			
	text(col='black',adj=c(0,NA),x=.015,y=cnts,labels=paste('(',str_sub(ss.dif,1,str_locate(ss.dif,pattern='[/.]')[1]+2),')',sep=''),srt = 1, pos = 2,cex=.5)				

}         

# lines and legend graphics
lines(col='grey',lty=2,y=c(.5,.5),x=c(-.0515,1.))  
lines(col='grey',lty=2,y=c(7.5,7.5),x=c(-.0515,1.))
lines(col='grey',lty=2,y=c(11.5,11.5),x=c(-.0515,1.)) 
lines(col='grey',lty=2,y=c(15.5,15.5),x=c(-.0515,1.)) 
lines(col='grey',lty=2,y=c(19.5,19.5),x=c(-.0515,1.))                                                  

axis(2,line=-1.5,at=c(.5,7.5,11.5,15.5,19.5),labels=F,tck=-.02)

text(x=1.-.0105,y=19.,labels='Democratic',cex=.75,adj=c(1,1),srt=0)
text(x=1.-.0105,y=15.,labels='Republican',cex=.75,adj=c(1,1),srt=0)
text(x=1.-.0105,y=11.,labels='Bipartisan',cex=.75,adj=c(1,1),srt=0)
text(x=1.-.0105,y=7.,labels='Court/\nIndependent',cex=.75,adj=c(1,1),srt=0)
mtext("Proportion of Swing Year Flips",side=1,at=.5,adj=.5,line=3)    
dev.off()
}

#end  